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Modelling heat load is a crucial challenge for the proper management of heat production and distribution. Several studies have tackled this issue at building and urban levels, however, the current scale of interest is shifting to the district level due to the new paradigm of the smart system. This study presents a stochastic procedure to model district heat load with a different number of buildings aggregation. The proposed method is based on a superimposition approach by analysing the seasonal component using a linear regression model on the outdoor temperature and the intra-daily component through a bi-parametric distribution of different times of the day. Moreover, an empirical relationship, that estimates the demand variation given the average demand together with a user aggregation coefficient, is proposed. To assess the effectiveness of the proposed methodology, the study of a group of residential users connected to the district heating system of Bozen-Bolzano is carried out. In addition, an application on a three-day prevision shows the suitability of this approach. The final purpose is to provide a flexible tool for district heat load characterisation and prevision based on a sample of time series data and summary information about the buildings belonging to the analysed district.

In this work, we focus on investigating the relationship between urban morphology parameters and residential building space heating energy performance, comparing microclimate conditions of existing residential blocks with central heating supply. Firstly, a dataset composed of district morphological parameters that measured heat energy consumption was established. Then, effects of morphological indicators including cover ratio, average building height, and floor area ratio on building space heating energy efficiency were assessed specifically. Analysis results show that a larger floor area ratio induced a reduction in heating energy consumption density, the observed effect is notable at an initial increase of floor area ratio. Thirdly, the case study shows that the heating load of residential districts with a high built density is more sensitive to solar radiation. To further assess how and to what extent urban forms alter microclimates, on-site measurement investigated detailed changes in the thermal environment of selected residential districts before and after the operational stage of central heating supply. Analysis results demonstrate that heat energy delivered by a central heating supply could dampen the variations of local outdoor air temperatures, more notable for residential districts with a higher floor area ratio during the night period. Findings from this work would be useful for urban planners considering energy-efficient design practices.

This article provides the state-of-the-art on the optimal planning and design of future district heating (DH) systems. The purpose is to provide practical information of first-step actions for countries with a low DH market share for heating and cooling supply. Previous research showed that for those countries, establishing a heat atlas with accurate geographical data is an essential prerequisite to promote the development of DH systems. In this review, essential techniques for building a high-quality heat atlas are elaborated. This includes a review of methodologies for district thermal energy demand prediction and the status of the integration of sustainable resources in DH systems. In the meanwhile, technical barriers for the implementation of various sustainable heat sources are identified. Furthermore, technologies for the optimal planning of DH systems are discussed. This includes the review of current approaches for the optimal planning of DH systems, discussions on various novel configurations which have been actively investigated recently, and common upgrading measures for existing DH systems.

The presented research covers the work carried out in the frame of the TCology project, conducted under funding of the Swiss Federal Office of Energy SFOE, evaluating the potential of thermochemical networks (TCNs) as an alternative to classical, hydronic district heating networks (DHN). The primary aim was to understand how TCNs can enhance the utilization of renewable energy in district heating systems through lossless energy distribution and long-term storage. The challenge was approached by numerical simulation of TCNs and 4th generation hydronic networks for performance comparison using Modelica as a modelling platform. Two case studies were defined for the performance comparison; i) space heating and domestic hot water application of 190 residential buildings / single family homes with a cumulated heat demand of 1.53 GWh per year and a peak power demand of 0.92 MW and ii) the application from i) extended with an industrial herb drying application with an annual heat demand of 0.1 GWh and a peak demand of 0.1 MW, featuring an open sorption process. The results identify biggest benefits for using TCN for low-temperature space heating applications with up to 5 times higher exergy efficiency and 2-3 times higher volumetric energy storage density compared to a classical DHN. Further, the possibility of providing the annual heat demand from a thermochemical storage with solar regeneration has been confirmed and quantified in terms of required storage volumes.

Reliability evaluation of park-level integrated energy systems (PIES) requires capturing the complex interactions among electricity, gas, thermal, and building subsystems, which exhibit typical multiscale dynamic behaviors. Although existing studies have partially considered the dynamic characteristics of PIES, the critical transient responses and the long-term impacts of heat supply interruptions on human health are overlooked. To address these gaps, this paper proposes a reliability evaluation method based on quasi-steady-state simulation. The dynamic behaviors of the electricity, gas, and heating systems, as well as buildings, are analyzed to develop simplified quasi-steady-state models for both energy demand under normal conditions and indoor temperature evolution under extreme scenarios. A new reliability index is then formulated by incorporating the thermal inertia of district heating networks and the temperature tolerance of occupants, forming a comprehensive framework for PIES reliability assessment. Case studies verify that the proposed method enhances the accuracy and interpretability of reliability evaluations, bridging the gap in user health considerations under extreme conditions. The results further reveal that building thermal dynamics play a dominant role in determining the overall reliability performance of PIES.

Fifth-generation district heating systems have appeared in the heating supply of modern cities. There is a growing demand for the use of renewable energy sources, the most significant of which is geothermal energy. Implementing district heating systems is very costly, and reducing investment and operating costs requires accurate forecasting of expected heating demands and uncertainty analysis. The design of district heating systems begins with determining the heat demand to be supplied. The heating demand maximum occurring with a 1% frequency (24-h duration) is called the representative heating demand. In our study, we deal with the probability theory of heating demands. Heating demands are also uncertain primarily due to the uncertainties in meteorological and weather characteristics and, in a mathematical sense, are random variables. We must emphasize that any description that follows a deterministic approach in determining heating demands does not meet the criteria of modern science and can lead to severe planning and operational errors. In our study, following a probabilistic approach, we present a mathematical model that allows the determination of heating demands using probabilistic tools. With the help of the model, we can determine the low-risk representative heat demand and, through this, assess whether the available heat production capacity is sufficient with the prescribed safety or whether its expansion is necessary. Furthermore, in operation, we can prepare with the least risk of the short- and long-term forecasted values of meteorological factors. In our study, using the presented probabilistic method, we proved that for the most common prefabricated housing units in Hungary (approximately 500,000), there is a 30% uncertainty in the representative—previously accepted as 5000 W—heat demand, which, if considered in operation, can avoid significant costs. Our investigations showed that under representative meteorological conditions, the standard deviation of the expected heat demand is 1119 W, while the expected value is 3700 W. With the help of the presented model, the errors and uncertainties in the parameters used to calculate heat demand can be extensively analyzed.

This paper aims to present an approach for the planning of carbon low heat supply in a future district heating system based on open data for German cities with existing district heating networks. One focus is on the integration of industrial waste heat and the uncertainty of future waste heat sources as well as restrictions on the use of biomass. For that purpose, knowledge about the energy demand is necessary. In a first step it is shown how the demand around a heating network is estimated with spatial data and a load profile is generated. Local available heat sources are examined according to their suitability and their kind of integration in the heating network. As heat production from different units are optimised, the development of a simulation model will be presented. The simulation is based on the optimisation of the operational costs of the used technologies for heating supply. Different scenarios covering various technologies and economic assumptions are applied. The results show the levelized costs of heating as well as the ecological performance. A sensitivity analysis shows the importance of uncertainties for the economic assumptions. The results showing levelized costs of heating as well as the ecological performance underlining the advantage of excess heat integration.

End-use efficiency, demand response and coupling of different energy vectors are important aspects of future renewable energy systems. Growth in the number of data centres is leading to an increase in electricity demand and the emergence of a new electricity-intensive industry. Studies on data centres and energy use have so far focused mainly on energy efficiency. This paper contributes with an assessment of the potential for energy system integration of data centres via demand response and waste heat utilization, and with a review of EU policies relevant to this. Waste heat utilization is mainly an option for data centres that are close to district heating systems. Flexible electricity demand can be achieved through temporal and spatial scheduling of data centre operations. This could provide more than 10 GW of demand response in the European electricity system in 2030. Most data centres also have auxiliary power systems employing batteries and stand-by diesel generators, which could potentially be used in power system balancing. These potentials have received little attention so far and have not yet been considered in policies concerning energy or data centres. Policies are needed to capture the potential societal benefits of energy system integration of data centres. In the EU, such policies are in their nascent phase and mainly focused on energy efficiency through the voluntary Code of Conduct and criteria under the EU Ecodesign Directive. Some research and development in the field of energy efficiency and integration is also supported through the EU Horizon 2020 programme. Our analysis shows that there is considerable potential for demand response and energy system integration. This motivates greater efforts in developing future policies, policy coordination, and changes in regulation, taxation and electricity market design.

Building footprints are a geographical indication of the spatial distribution of built-up infrastructure, thereby reflecting energy demand patterns, including heating requirements. Heating demands spatial distribution shown in heat atlases are primordial for evaluating district heating systems feasibility, which are a key decarbonizing technology that offers more sustainable heat supply in dense urban areas. Sustainable energy planning frameworks utilize district heating potentials as metrics for the formulation of alternate system configurations aimed at decarbonizing societies and creating an understanding of heating transition pathways. However, the availability and accessibility of the data needed for assessing these potentials is highly contextual and often challenges modelling processes. Simultaneously, there is a growing potential for open data and software mechanisms that could aid in addressing these challenges and create otherwise unavailable heat mapping resources. This paper describes the development of the ODHeatMap tool, a workflow built with open data in python functions that transform building footprints into a heat atlas. Ulaanbaatar city is used as a demonstration area for the tools functionalities, with the outputs being applied in a broader study aimed at developing strategies for Mongolia's heating sector. The tool is accessible through a fully cloud-based environment and can be used in any given geographical context.

This paper presents a district-scale energy modeling framework using City Energy Analyst (CEA), applied to a real-world case in Odense, Denmark. The study assesses cost-effective retrofits and explores the feasibility of achieving PED status, aligning with Net Zero Emissions targets. A case study of the Rasmus Rask Kollegiet (RRK) dormitory complex, consisting of 60 buildings constructed between 1974 and 1985, is examined. The district relies on district heating and the Danish electrical grid, with outdated heating systems and insufficient mechanical ventilation contributing to energy inefficiencies. The CEA tool, an open-source, Python-based simulation platform, is used to model energy demand, evaluate renovation scenarios, and optimize energy supply strategies. The base case modeling demonstrated that RRK’s energy performance model was accurate, with heating demand estimates deviating by less than 3% from the 2023 energy labeling report. The study evaluated single-measure upgrades to building envelopes and energy supply systems, followed by six renovation packages combining envelope improvements with advanced energy technologies such as heat pumps, photovoltaic (PV), photovoltaic-thermal (PVT), and solar collectors. Simulation results show that these integrated measures significantly reduce energy consumption, emissions, and enhance system efficiency. The final renovation package not only reduces demand but also generates more renewable energy than the district consumes annually, achieving PED status. These findings highlight the feasibility of establishing Danish PEDs, supporting Denmark’s 2050 climate goals and advancing sustainable, district-scale energy transitions.